OPTIMIZED CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR IMAGE RECOGNITION BASED ON ARTIFICIAL INTELLIGENCE. (ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ, 2025)
This article proposes a convolutional neural network (CNN) architecture optimized for automatic image processing and feature extraction using artificial intelligence (AI) and deep learning technologies. During the research, existing CNN models such as ResNet, DenseNet, and EfficientNet were analyzed to identify their strengths and weaknesses. Based on these findings, optimization strategies were developed by adjusting the number of layers and kernel sizes, selecting pooling layers and activation functions, and applying transfer learning and quantization methods to improve computational efficiency.